~~IT market studies expert indicate that the advanced analysis of various data and the management of an increasing volume of data, are among the five priorities large enterprises CIOs and companies at the forefront of the world of internet. Big Data is thus increasingly important, and a growing number of companies complete their decision-making infrastructure, with new analytical platforms to improve their efficiency and profitability. First experiences Big Data observations show that many different technologies are used, even if an emerging technology based on the open source project Apache Hadoop, is often present in the infrastructure of the pioneers of Big Data. Hadoop's popularity seems to lie in its ability to handle large volumes of data with a standard low cost server infrastructure. But be careful all the experts say that there is no universal solutions for Big Data, users should be determined according to their needs, the appropriate technology mix to put in place, and set precisely where each (including Hadoop) can add value in their decision-making architecture. First, consider the data that are to be processed. There are those in which the data model is established and is stable over time. Here we will find everything about the classic BI, reporting, financial analysis, automated decisions related to the operational and analysis of spatial data. There are those that can be stored in a more or less raw mode, which will be modeled in different ways according to the needs of iterative analyzes. This may involve, for example, the analysis of user clicks during their web browsing, data from sensors, CDR in telecommunications. There are those who are simply defined by a format. It is for example: images, videos, and audio recordings. We must also consider what you want to do with the data. If the idea is to use structured data (reporting, BI, data mining), a classical appliance will be ideal, you can possibly complete with a cheap storage Hadoop solution appliance or with "the Teradata Extreme Data Appliance” for the data which present less interest in being included in the model of the warehouse business. For other data (web log, sensors, CDR, images, videos, ...) must be used according to the treatments described in the type Hadoop solutions and / or Teradata Aster, which can be implemented at low cost ( storage, application development, operation, integration with structured data warehouse) MapReduce programs. Note that the Teradata Aster uses a patented technology named SQL-MapReduce that allows implementing MapReduce programs without having to learn a new programming language. This solution also offers the performance, scalability to handle large volumes of data, and process data with relational data pertaining to various formats. Compared with Hadoop this solution offers substantial benefits in terms of development costs and application response time. Measured by an increase in revenue, market share gains and cost reduction, data analyses have always played a key role in business success. Today, the development of the Internet and automated business processes, makes crucial Big Data operations, and bring business leaders to rely more and more on their data analysis means. In this context, the teams are then conducted to supplement their existing infrastructure decision-making, with new solutions that implement complex algorithms. The pioneers who have already operated Big Data successfully, all say there is no magic bullet, even Hadoop, and it is for that Teradata offers different platforms implementing the Teradata database, Aster MapReduce and Hadoop solutions. To go further on the subject you can effectively discover the latest Teradata Aster Big Analytics Appliance that integrates in a single platform solution Hadoop and Aster (Hortonworks distribution): http://www.asterdata.com/resources/downloads/datasheets/Teradata-As...
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